
'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__ :			Tuple											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Union[str, Any]											=		"▁"

UpperCAmelCase__ :			List[str]											=		{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__ :			Union[str, Any]											=		{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
UpperCAmelCase__ :			Tuple											=		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  VOCAB_FILES_NAMES
	snake_case__   :str   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Any   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Tuple   =  ['input_ids', 'attention_mask']

	snake_case__   :List[int]   =  []
	snake_case__   :List[int]   =  []





	def __init__(       self      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[int]="</s>"   ,				__magic_name__      :							List[Any]="</s>"   ,				__magic_name__      :							List[Any]="<s>"   ,				__magic_name__      :							Tuple="<unk>"   ,				__magic_name__      :							List[Any]="<pad>"   ,				__magic_name__      :							List[Any]="<mask>"   ,				__magic_name__      :							Optional[Dict[str, Any]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				AddedToken(__magic_name__   ,				lstrip=__magic_name__   ,				rstrip=__magic_name__				) if isinstance(__magic_name__   ,				__magic_name__				) else mask_token

				lowerCAmelCase__										=				{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCAmelCase__										=				kwargs.get("additional_special_tokens"   ,				[]				)
				kwargs["additional_special_tokens"] += [
				    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__magic_name__   ,				tgt_lang=__magic_name__   ,				eos_token=__magic_name__   ,				unk_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				pad_token=__magic_name__   ,				mask_token=__magic_name__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(str(__magic_name__				)				)
				lowerCAmelCase__										=				vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowerCAmelCase__										=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowerCAmelCase__										=				1

				lowerCAmelCase__										=				len(self.sp_model				)
				lowerCAmelCase__										=				{
				    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__magic_name__				)
				}
				lowerCAmelCase__										=				{v: k for k, v in self.lang_code_to_id.items()}
				lowerCAmelCase__										=				len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

				self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
				lowerCAmelCase__										=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

				lowerCAmelCase__										=				src_lang if src_lang is not None else "en_XX"
				lowerCAmelCase__										=				self.lang_code_to_id[self._src_lang]
				lowerCAmelCase__										=				tgt_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self._src_lang





	@src_lang.setter
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	def __getstate__(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.__dict__.copy()
				lowerCAmelCase__										=				None
				return state





	def __setstate__(       self      :							List[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				d

				# for backward compatibility
				if not hasattr(self   ,				"sp_model_kwargs"				):
							lowerCAmelCase__										=				{}

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(self.vocab_file				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				{self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
				vocab.update(self.added_tokens_encoder				)
				return vocab





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							str				):


				"""simple docstring"""

				return self.sp_model.encode(__magic_name__   ,				out_type=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				lowerCAmelCase__										=				self.sp_model.PieceToId(__magic_name__				)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							int				):


				"""simple docstring"""

				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				lowerCAmelCase__										=				""
				lowerCAmelCase__										=				False
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										if not prev_is_special:
													out_string += " "
										out_string += self.sp_model.decode(__magic_name__				) + token
										lowerCAmelCase__										=				True
										lowerCAmelCase__										=				[]
							else:
										current_sub_tokens.append(__magic_name__				)
										lowerCAmelCase__										=				False
				out_string += self.sp_model.decode(__magic_name__				)
				return out_string.strip()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

				if not os.path.isdir(__magic_name__				):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
							return
				lowerCAmelCase__										=				os.path.join(
				    __magic_name__   ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
							copyfile(self.vocab_file   ,				__magic_name__				)
				elif not os.path.isfile(self.vocab_file				):
							with open(__magic_name__   ,				"wb"				) as fi:
										lowerCAmelCase__										=				self.sp_model.serialized_model_proto()
										fi.write(__magic_name__				)

				return (out_vocab_file,)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None   ,				__magic_name__      :							bool = False				):


				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__magic_name__   ,				token_ids_a=__magic_name__   ,				already_has_special_tokens=__magic_name__				)

				lowerCAmelCase__										=				[1] * len(self.prefix_tokens				)
				lowerCAmelCase__										=				[1] * len(self.suffix_tokens				)
				if token_ids_a is None:
							return prefix_ones + ([0] * len(__magic_name__				)) + suffix_ones
				return prefix_ones + ([0] * len(__magic_name__				)) + ([0] * len(__magic_name__				)) + suffix_ones





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str]   ,				__magic_name__      :							Optional[str]   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				self(__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)
				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)
				lowerCAmelCase__										=				tgt_lang_id
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str = "en_XX"   ,				__magic_name__      :							Optional[List[str]] = None   ,				__magic_name__      :							str = "ro_RO"   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				tgt_lang
				return super().prepare_seqaseq_batch(__magic_name__   ,				__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return self.set_src_lang_special_tokens(self.src_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return self.set_tgt_lang_special_tokens(self.tgt_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[src_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[tgt_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]










'''simple docstring'''



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'     ,					'False'      )      ) is not True     ,					reason='Skipping test because should only be run when releasing minor transformers version'     ,					)
@pytest.mark.usefixtures('sm_env'      )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]      )
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													if self.framework == "pytorch":
																				subprocess.run(
																				    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,     encoding='''utf-8'''					,     check=lowerCAmelCase__					,     )
													assert hasattr(self					,     '''env'''					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# configuration for running training on smdistributed Model Parallel
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''enabled''': True,
													    '''processes_per_host''': 8,
													}
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''enabled''': True,
													    '''parameters''': {
													        '''microbatches''': 4,
													        '''placement_strategy''': '''spread''',
													        '''pipeline''': '''interleaved''',
													        '''optimize''': '''speed''',
													        '''partitions''': 4,
													        '''ddp''': True,
													    },
													}

													SCREAMING_SNAKE_CASE       :   str         =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
													# creates estimator
													return HuggingFace(
													    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""					,     instance_count=lowerCAmelCase__					,     instance_type=self.instance_type					,     debugger_hook_config=lowerCAmelCase__					,     hyperparameters={
													        **self.env.hyperparameters,
													        '''model_name_or_path''': self.model_name_or_path,
													        '''max_steps''': 5_00,
													    }					,     metric_definitions=self.env.metric_definitions					,     distribution=lowerCAmelCase__					,     py_version='''py36'''					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													TrainingJobAnalytics(lowerCAmelCase__					).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""					)







						@parameterized.expand([(1,)]					)
						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# create estimator
													SCREAMING_SNAKE_CASE       :   Any         =      self.create_estimator(lowerCAmelCase__					)

													# run training
													estimator.fit()

													# result dataframe
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

													# extract kpis
													SCREAMING_SNAKE_CASE       :   int         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']					)
													# get train time from SageMaker job, this includes starting, preprocessing, stopping
													SCREAMING_SNAKE_CASE       :   int         =      (
													    Session().describe_training_job(estimator.latest_training_job.name					).get('''TrainingTimeInSeconds'''					,     99_99_99					)
													)

													# assert kpis
													assert train_runtime <= self.results["train_runtime"]
													assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy					)
													assert all(t <= self.results['''eval_loss'''] for t in eval_loss					)

													# dump tests result into json file to share in PR
													with open(F"""{estimator.latest_training_job.name}.json"""					,     '''w'''					) as outfile:
																				json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,     lowerCAmelCase__					)




